A naive way to make such models provably forgotten is to retrain them from scratch.
However, the computational and time overhead associated with fully retraining models affected by training data erasure can be prohibitively expensive [1]}.
Approximate unlearning methods produce ML models that are an approximation of the fully retrained models.
Most of them can be categorized into one of three groups.
Given the category \(\mathbf {i}\)  to be unlearned from the trained model.
The first group updates the trained ML model using the data \(\mathbf {D_i}\)  of \(\mathbf {i}\)  to perform a corrective Newton step; it is defined in [2]}.
The second group updates the trained ML model using the remaining data \(\mathbf {D}\) -\(\mathbf {D_i}\)  to perform a corrective Newton step; it follows [3]}, [4]}, [5]}.
The third group updates the trained ML model by correcting the SGD steps that led to the trained model; it follows the method defined in [6]}, [7]}.
Note that, the premise for above three groups to compute an accurate approximation is to obtain accurate \(\mathbf {D}\)  and \(\mathbf {D_i}\) , while both of data cannot be accessed globally in the FL settings.
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